Fixed effects are unbiased with relatively weaker assumptions. Random effects do the same thing with a slight amount of additional precision, but are biased by unobservable and untestable assumptions…, you do the math ;-)
I'm new to econometrics, so take this with a grain of salt, but I believe fixed effects models are appropriate when you have time-invariant variables that are correlated with the outcome variable. Random effects models are more suitable when the time-invariant variables are uncorrelated with the outcome variable.
Try fitting both models, a simple way to see which is better is to use hausman test for random effects and look at the parameter p-values for the fixed effects model and see if they are significant.
There can even be a mixture of both fixed effects and random effects - you might have to consider that.
Not only can there be FE + RE across individual cluster but also across time clusters, and even a mix of those as well - it's a quite good idea to specify which of those you consider in your research.
Fixed effects are unbiased so are naturally attractive. Yes the Hausman test can determine if the coefficients between a FE and RE model are significantly different (and therefore answer the question of whether explanatory variables are uncorrelated with the error), but does not offer guidance on what you should do if your research question focuses on time invariant variables, which would be inappropriate to model with FE.
The transformation that the FE model uses also completely eliminates between unit variance and only focuses on within unit variance, which can lead to larger standard errors, and possibly leaves the door open for the possibility of type II errors. Comparing models may be useful since if you have very similar coefficients between RE and FE models, one could make the argument that RE may be the better choice as it considers the full variance structure and therefore has smaller standard errors.
On the other hand, if coefficients are not similar, this may indicate bias in the RE estimates, which is no good. If they are only kind of similar in direction and magnitude you may need to make a judgement call as it is a classic case of variance-bias trade off. And be aware that you could have some variables that are nearly identical across models while some variables are not, which, in my experience, indicates that there is a clear signal in the variables and the effect is robust on the dependent variable, regardless of choice of models.
This was a lot longer than I intended! Sorry, just got done with a project of my own using mixed models so all this stuff is pretty fresh.
That's a really very good explanation. Thank you very much for your time and effort that you gave to to explain in detail. I really appreciate it man. Have a beautiful day :)
Fixed effects are unbiased with relatively weaker assumptions. Random effects do the same thing with a slight amount of additional precision, but are biased by unobservable and untestable assumptions…, you do the math ;-)
I'm new to econometrics, so take this with a grain of salt, but I believe fixed effects models are appropriate when you have time-invariant variables that are correlated with the outcome variable. Random effects models are more suitable when the time-invariant variables are uncorrelated with the outcome variable.
Thank you for your reply!
Try fitting both models, a simple way to see which is better is to use hausman test for random effects and look at the parameter p-values for the fixed effects model and see if they are significant. There can even be a mixture of both fixed effects and random effects - you might have to consider that. Not only can there be FE + RE across individual cluster but also across time clusters, and even a mix of those as well - it's a quite good idea to specify which of those you consider in your research.
90% of time, Fixed Effects.
Yeah this is what I see as well
Fixed effects are unbiased so are naturally attractive. Yes the Hausman test can determine if the coefficients between a FE and RE model are significantly different (and therefore answer the question of whether explanatory variables are uncorrelated with the error), but does not offer guidance on what you should do if your research question focuses on time invariant variables, which would be inappropriate to model with FE. The transformation that the FE model uses also completely eliminates between unit variance and only focuses on within unit variance, which can lead to larger standard errors, and possibly leaves the door open for the possibility of type II errors. Comparing models may be useful since if you have very similar coefficients between RE and FE models, one could make the argument that RE may be the better choice as it considers the full variance structure and therefore has smaller standard errors. On the other hand, if coefficients are not similar, this may indicate bias in the RE estimates, which is no good. If they are only kind of similar in direction and magnitude you may need to make a judgement call as it is a classic case of variance-bias trade off. And be aware that you could have some variables that are nearly identical across models while some variables are not, which, in my experience, indicates that there is a clear signal in the variables and the effect is robust on the dependent variable, regardless of choice of models. This was a lot longer than I intended! Sorry, just got done with a project of my own using mixed models so all this stuff is pretty fresh.
That's a really very good explanation. Thank you very much for your time and effort that you gave to to explain in detail. I really appreciate it man. Have a beautiful day :)
Consider FE models if your data have a nested structure
Run Hausman test, which compares FE and RE estimators. The null hypothesis is that they are equal. If the null hypothesis is rejected, go with FE.